Audio scene capture and rendering is a developing technical field. In audio scene capture multiple spaced microphones are used to capture audio signals which are compressed prior to being transmitted to rendering systems. Audio scene rendering is where the audio signal is decompressed and processed to render the audio such that the user of the audio rendering system can produce a customised aural “view point”. However to enable selection flexibility the capture and compression of the audio scene into a suitable format typically requires a significant amount of data to be passed across the network (or to be stored to be used at a later date), as multiple microphone configurations are captured in order to produce significantly detailed recordings. This produces bandwidth requirements on the network/storage system, or requires significant encoding efficiency to reduce the requirements.
Typical Nyquist sampling of each microphone audio signals to represent the captured audio signals requires a large number of samples and associated data which is then compressed using a suitable encoding algorithm in order to reduce the bandwidth required on the network or storage device.
Compressed or compressive sensing (CS) is an emerging and promising technology which has been shown to be useful in compressing image data more efficiently or effectively than conventional Cosine or Wavelet compression algorithms have achieved. Compressed sensing is attractive for compression purposes due to its relative lack of complexity at the capture side. Compressed sensing seeks to represent a signal using a number of linear, non-adaptive measurements. Typically the number of measurements is fewer than the number of samples needed where the signal is sampled at the Nyquist rate, thus providing the benefits of reduced storage space and transmission bandwidth encoding or compression. Compressed sensing requires that the signal is very sparse in some basis. In order words that the audio signals are a linear combination of a small number of basis functions. Where the audio signal is very sparse it is possible to correctly reconstruct the original signal despite the original signal not being sampled at the Nyquist rate. However the compressed or compressive sensing measurements made are usually not dependent on the basis used in reconstruction and thus the measurement process is universal as it does not need to change as different types of signals are captured.
However application of compressed sensing to audio signals is typically problematic in that audio signals are not truly sparse by nature. As such it has been difficult to apply compressed sensing to audio signals at compression ratios which produce good levels of compression with good levels of quality of reconstructed signal.